STRATAHELM · COMMUNITY AI EXPOSURE & RESILIENCE INDEX

Swift County, Minnesota

CAERI NationalMinnesota › Swift County · FIPS 27151 · caeri:27151
49.3
CAERI score (national basis)
25th–50th
National percentile band
#1618
Ranked of 3142 U.S. counties
#25
Ranked in Minnesota (of 87)
Medium
Confidence

Two comparisons, both shown. The national basis ranks Swift County against every scored U.S. county — its percentile band above is a relative national standing, not a condition. The in-state basis ranks it against Minnesota's other counties (rank and percentile only — bands aren't meaningful within a single state). A county can stand out in its own state while sitting mid-pack nationally. Neither is a forecast.

Pillar breakdown — national percentile ranks

P1 · Direct occupational exposure
43
P2 · Economic concentration
65
P3 · Adaptive capacity (inverted)
36
P4 · Regional buffer (inverted)
72
P5 · Fiscal sensitivity
23
Zone shading marks percentile quartiles; darker and warmer means more concerning. Capacity and buffer bars (inverted before aggregation) read mirrored — for those, a higher percentile is the safer pale end.

What this means for Swift County

Swift County scores 49.3 on the national CAERI basis — 1618th of 3,142 scored U.S. counties, placing it in the 25th–50th band nationally by percentile, with a medium-confidence rating. Within Minnesota it ranks 25th of 87 counties (72th in-state percentile). The score summarizes how concentrated local employment is in AI-exposed occupations against the economy's measured capacity to adapt; these are relative-standing bands, not a projection of local job change.

Direct occupational exposure stands at the 43rd national percentile. The largest concentrations of locally estimated employment in high-exposure occupations are office clerks, general, bookkeeping, accounting, and auditing clerks, secretaries and administrative assistants, except legal, medical, and executive. The share of exposed-industry jobs held by workers under 25 is 11% — above the state median of 10%; research on AI-era payrolls finds early-career roles in exposed work are where hiring patterns shift first, so this share indicates how soon exposure could be felt, not how large it is.

The share of exposed-industry jobs held by workers 55 and over is 31% (above the state median) — a higher share historically means slower workforce adjustment when industries restructure. Fiscal sensitivity ranks at the 45th state percentile. In Minnesota the transmission runs through the property-tax base — commercial-industrial property is taxed at higher classification rates than homesteads, so softness in commercial values shifts levy burden or squeezes capacity — and through state aid, 14% of general revenue here (well below the state median): Local Government Aid is financed from the state general fund and moves with statewide economic conditions, not only local ones. Local-option sales taxes are a minimal share of revenue here, leaving property values and state aid as the channels that matter. Minnesota local governments levy no local income tax. All county occupation figures on this page are model-based ESTIMATES with the confidence rating shown above.

Largest locally-estimated employment in high-exposure occupations

Across all 184 high-exposure occupations (top quartile of ensemble exposure) with estimated local employment, Swift County has an estimated 895 jobs — 20.9% of county employment — carrying an estimated $56M annual wage bill in high-exposure work. The five largest:

OccupationEst. local employment*Exposure (0–1)Median wage (area)
Office Clerks, General970.44$44,150
Bookkeeping, Accounting, and Auditing Clerks610.51$48,500
Secretaries and Administrative Assistants, Except Legal, Medical, and Executive590.50$46,620
Customer Service Representatives540.42$43,450
Elementary School Teachers, Except Special Education500.43$61,590
*County-level occupation figures are model-based ESTIMATES, not surveyed counts (see methodology §5 summary); every county carries the confidence rating shown above. Wage = area median.

Age structure of exposed-industry employment

Swift CountyMinnesota median
Share of exposed-industry jobs held by workers under 25 (entry rung) 11.0%10.0%
Share of exposed-industry jobs held by workers 55+ (adjustment friction) 31.3%28.6%

🔒 City-level detail for its cities and towns

The county number above averages over every community in it. City- and place-level exposure profiles, employer-mix detail, trend monitoring, and peer benchmarking are part of the CAERI subscription for local governments and regional organizations.

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Cite this page

Canonical identifier: caeri:27151 — this URL is permanent; if the address scheme ever changes, the old address will redirect. StrataHelm. (2026). Swift County, Minnesota — Community AI Exposure & Resilience Index (CAERI v0.3.1) [Data set]. Retrieved August 4, 2026, from https://stratahelm.com/counties/minnesota/swift/ “Swift County, Minnesota — CAERI.” StrataHelm, 2026, stratahelm.com/counties/minnesota/swift/. Accessed 4 August 2026. StrataHelm. “Swift County, Minnesota — Community AI Exposure and Resilience Index (v0.3.1).” 2026. https://stratahelm.com/counties/minnesota/swift/
Embed this county's score card — attribution to StrataHelm CAERI and the link back are part of the embedded page and cannot be stripped:<iframe src="https://stratahelm.com/counties/minnesota/swift/embed/" width="420" height="275" loading="lazy" title="CAERI — Swift County, MN"></iframe>

Machine-readable: this county's JSON · national dataset download and data dictionary on the methodology page.

Nearby and comparable counties

Chisago Co., MN (in state)Wabasha Co., MN (in state)Anoka Co., MN (in state)Harrison Co., IN (similar score)Putnam Co., OH (similar score)Hyde Co., SD (similar score)

Links point to the economically nearest counties (in-state, and similar national score). County-level geographic adjacency is not part of the public reference data, so proximity here is by rank, not by shared border.

Sources & provenance

LayerSources (vintage)
Employment & occupation structurecbp_mn_county_naics4: Census CBP 2023 API (NAICS2017 classification; PAYANN in $1,000s; noise infusion G/H/J bands, D = withheld -&gt; NaN); county_soc_estimates.meta.json; exposure_scores: 2026-07-04; matrix_staffing_patterns: 2024-34 National Employment Matrix (base year 2024)
AI-exposure research baseAnthropic Economic Index; Felten, Raj & Seamans (AIOE); Eloundou et al. — combined as a weighted ensemble with cross-source disagreement feeding the confidence rating
Capacity, buffer & fiscalgovfin_mn_county_revenue_mix: 2022 Census of Governments, Survey of Government Finances; lodes_mn_county_flows: LEHD LODES8 OD 2023, JT00/S000; sources: mn main+aux, wi/nd/sd/ia aux; qwi_mn_county_naics3_age: Census QWI (qwi/sa), quarters [&#x27;2024-Q4&#x27;, &#x27;2025-Q1&#x27;, &#x27;2025-Q2&#x27;, &#x27;2025-Q3&#x27;], sex=0, ownercode=A05 (private), 96 NAICS-3 + &#x27;00&#x27; all-industry
MethodologyCAERI v0.3.1 · public methodology · scores generated 2026-07-12 · page generated 2026-08-04
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